EP1816451B1 - Procédé et appareil de diagnostic d'anomalies pour son ou vibration - Google Patents

Procédé et appareil de diagnostic d'anomalies pour son ou vibration Download PDF

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Publication number
EP1816451B1
EP1816451B1 EP07290125.9A EP07290125A EP1816451B1 EP 1816451 B1 EP1816451 B1 EP 1816451B1 EP 07290125 A EP07290125 A EP 07290125A EP 1816451 B1 EP1816451 B1 EP 1816451B1
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Prior art keywords
vibration
sound
abnormality
sources
mother wavelet
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German (de)
English (en)
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EP1816451A2 (fr
EP1816451A3 (fr
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Hideaki c/o JTEKT Corporation Ishii
Zhong Zhang
Hiroshi c/o JTEKT Corporation Uemura
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ZHANG, ZHONG
JTEKT Corp
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JTEKT Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
    • G01H3/10—Amplitude; Power
    • G01H3/12—Amplitude; Power by electric means
    • G01H3/125—Amplitude; Power by electric means for representing acoustic field distribution

Definitions

  • the present invention relates to abnormality diagnosing method and apparatus for sound or vibration that allow the presence or absence of abnormality in a plurality of sound sources or vibration sources to be diagnosed on the basis of an analysis result of sound or vibration at a single evaluation point.
  • a widely adopted analysis method is a method based on the FFT (Fast Fourier Transform).
  • FFT Fast Fourier Transform
  • a detection result of sound or vibration acquired as a waveform signal is processed by Fourier transformation, so that spectral intensity is obtained for each frequency component.
  • Each of a detection signal of sound or vibration at an evaluation point set up in a target space and detection signals of sound or vibration at a plurality of candidate points expected to be a sound source or vibration source is processed by Fourier transformation. Then, the spectrum distribution at each candidate point is compared with the spectrum distribution at the evaluation point, so that a sound source or vibration source is identified.
  • an analysis method is based on the wavelet analysis.
  • An abnormality diagnosing apparatus employing this method is proposed in the prior art (see, for example, Japanese Patent Publication No. 3561151 ).
  • This abnormality diagnosing apparatus detects a waveform signal generated in a target object, then performs wavelet transformation, and thereby performs abnormality diagnosis of the target object on the basis of the result of the transformation. More specifically, this abnormality diagnosing apparatus is provided with analysis function determining means that automatically derives a mother wavelet appropriate for the abnormality diagnosis of the target object on the basis of the input waveform signal.
  • the abnormality diagnosing apparatus performs wavelet transformation on the waveform signal detected by a sensor, and then performs abnormality diagnosis of the target object on the basis of the transformation result.
  • the analysis function determining means performs Fourier transformation on a plurality of waveform signals detected by the sensor, then performs inverse Fourier transformation on the average Fourier data obtained by averaging the Fourier transformation results, and thereby derives a mother wavelet.
  • An analysis method employing FFT is applicable for finding abnormality in a sound source or vibration source. For example, data of noise acquired at the time of various kinds of abnormality may be prepared in advance. Then, Fourier transformation may be performed respectively on this data and a detection result of noise acquired in real time. According to this, abnormality in each section can be determined using the detection result of the noise acquired at a particular evaluation point.
  • this analysis method is an analysis method not employing time information.
  • the noise signals generated at the time of abnormality in a plurality of sound sources or vibration sources are in the same or near frequency range, it is difficult to identify correctly the abnormality occurrence part.
  • a countermeasure is taken at any one of the candidate points, a possibility arises that the countermeasure may be inappropriate.
  • countermeasures are taken at all the candidate points, excessive and intrinsically unnecessary check and countermeasures are required at a plurality of the candidate points. This causes a problem.
  • the abnormality diagnosing apparatus described in Japanese Patent Publication No. 3561151 adopts wavelet analysis employing time information. Nevertheless, the mother wavelet derived by the analysis function determining means is shifted to a high frequency side or a low frequency side by the characteristic frequency in the scale. Thus, the wavelet coefficient calculated in the wavelet transformation using the enlarged or contracted mother wavelet hardly represents appropriate correspondence to abnormality in the target object. This causes a problem of poor precision in the abnormality diagnosis.
  • An object of the present invention is to provide abnormality diagnosing method and apparatus in which an analysis method employing time information is adopted so that generation of abnormality in a plurality of sound sources or vibration sources can be diagnosed correctly on the basis of an analysis result of sound or vibration at a particular evaluation point.
  • the abnormality diagnosing method for sound or vibration is an abnormality diagnosing method for sound or vibration for analyzing sound or vibration that appears at an evaluation point set up in a target space containing a plurality of sound sources or vibration sources, and thereby diagnosing the presence or absence of abnormality occurring in the sound source or vibration source, characterized by comprising: a preliminary step of extracting abnormal waveform signals individually from a detection result of sound or vibration acquired in advance at the evaluation point at the time of occurrence of abnormality in each of the plurality of sound sources or vibration sources; a first step of extracting an evaluation waveform signal serving as an analysis target from a detection result of sound or vibration at the evaluation point; a second step of performing Hanning window processing, Fourier transformation, normalization processing, and Hilbert transformation on the evaluation waveform signal extracted at the first step, and thereby deriving a real signal mother wavelet of complex type; and a third step of performing wavelet transformation on each of the abnormal waveform signals extracted at the preliminary step by using the real signal mother wavelet derived at the second step
  • the abnormality diagnosing apparatus for sound or vibration is an abnormality diagnosing apparatus for sound or vibration for analyzing sound or vibration that appears at an evaluation point set up in a target space containing a plurality of sound sources or vibration sources, and thereby diagnosing the presence or absence of abnormality occurring in the sound source or vibration source, characterized by comprising: detecting means for sound or vibration arranged at the evaluation point ; storage means for storing abnormal waveform signals individually extracted from a detection result of the detecting means at the time of occurrence of abnormality in each of the plurality of sound sources or vibration sources; extracting means for extracting an evaluation waveform signal serving as an analysis target from the detection result of the detecting means; deriving means for performing Hanning window processing, Fourier transformation, normalization processing, and Hilbert transformation on the evaluation waveform signal extracted by the extracting means, and thereby deriving a real signal mother wavelet of complex type; correlation calculating means for performing wavelet transformation on each of the abnormal waveform signals stored in the storage means by using the real signal mother wavelet derived by
  • abnormal waveform signals waveform signals to be evaluated
  • a real signal mother wavelet of complex type is derived using an evaluation waveform signal (sample waveform signal) extracted from a detection result of sound or vibration acquired at the evaluation point in real time. Then, using this real signal mother wavelet, wavelet transformation is performed on the abnormal waveform signals, so that a correlation value containing time information is calculated between each abnormal waveform signal and the evaluation waveform signal.
  • abnormality diagnosis for a plurality of sound sources or vibration sources can be implemented with precision on the basis of the detection result of sound or vibration at the evaluation point.
  • an appropriate countermeasure can be performed without excess or deficiency. This avoids transition to a serious abnormality and occurrence of an accident caused by this serious abnormality.
  • FIG. 1 is an explanatory view showing a state of implementation of an abnormality diagnosing method for sound or vibration according to the present invention.
  • a state of implementation of an abnormality diagnosing method is illustrated, where a vibration source that causes abnormal noise generated inside a car cabin of an automobile is identified among the vibration sources located in individual sections of an electric power steering device provided in the automobile.
  • the electric power steering device has a steering mechanism of rack and pinion type provided with a rack shaft 1 supported in a movable manner in the axial length direction in the inside of a rack housing 10 that extends in the right and left directions of a car body and with a pinion shaft 2 supported in a rotatable manner in the inside of a pinion housing 20 that intersects with the middle of the rack housing 10.
  • the two ends of the rack shaft 1 that protrude outward from both sides of the rack housing 10 are linked through tie rods 11 and 11 respectively to right and left front wheels 12 and 12 serving as steerable wheel.
  • the upper end of the pinion shaft 2 that protrudes to the outside of the pinion housing 20 is linked through a steering shaft 3 to a steering wheel 30 serving as a steering member.
  • a pinion (not shown) is formed in the lower part of the pinion shaft 2 that extends inside the pinion housing 20. In a part intersecting with the rack housing 10, the pinion engages with the rack teeth formed over an appropriate length on the external surface of the rack shaft 1.
  • the steering shaft 3 is supported in a rotatable manner inside a column housing 31 having a cylinder shape, and then attached inside the car cabin via the column housing 31 in an inclined arrangement with the front side down.
  • the steering wheel 30 is fixed to a protruding end of the steering shaft 3 toward the upper part of the column housing 31.
  • a pinion shaft 2 is linked to a lower protruding end.
  • a torque sensor 4 is provided for detecting a steering torque applied to the steering shaft 3 by the rotatory operation of the steering wheel 30. Further, a steering assistance motor 5 is attached at a position lower than the torque sensor 4.
  • the torque sensor 4 has a publicly known configuration in which the steering shaft 3 serving as the target for detection is divided into two up and down shafts while these two shafts are linked together on the same axis with a torsion bar having known torsion characteristics, and in which relative angular displacement generated by the operation of a steering torque between the two shafts that associates with a torsion in the torsion bar is detected by appropriate means. Further, the steering assistance motor 5 is attached outside the column housing 31 in a manner that its shaft axes intersect with each other approximately at rectangles.
  • a worm fixed to the output end that extends inside the column housing 31 engages with the worm wheel fit and fixed to the outside of the steering shaft 3, so that the rotation of the motor 5 is transmitted to the steering shaft 3 with predetermined speed reduction via a transmission mechanism provided with a worm and a worm wheel.
  • the steering assistance motor 5 attached in this manner is driven in accordance with the direction and the magnitude of the steering torque detected by the torque sensor 4. At that time, the rotating force generated by the motor 5 is applied to the pinion shaft 2 linked to the lower end of the steering shaft 3, so that the rotating force assists the steering performed as described above.
  • vibration occurs in the periphery of a part in which relative displacement is generated between the members, like in the periphery of the transmission mechanism that transmits the rotation of the steering assistance motor 5 to the steering shaft 3 with speed reduction, the periphery of the engagement part between the pinion shaft 2 and the rack shaft 1, and the periphery of the support part for supporting the rack shaft 1 in a slidable manner at one side-end part of the rack housing 10.
  • This vibration propagates and is then heard in the car cabin.
  • the abnormality diagnosing method for sound or vibration addresses the sound generated in the car cabin as described above, and aims at diagnosing the presence or absence of abnormality in each of the above-mentioned vibration sources serving as a generation source of this sound.
  • an evaluation point is defined at an appropriate position inside the car cabin, for example, in the periphery of the driver who operates the steering wheel 30.
  • a detector such as a microphone 6 for sound detection is arranged at this evaluation point.
  • a sound detection signal of this microphone 6 is provided to the abnormality diagnosing apparatus 7.
  • the abnormality diagnosing apparatus 7 performs the following operation.
  • a vibration sensor may be used in place of the microphone 6.
  • FIG. 2 is a block diagram showing an internal configuration of the abnormality diagnosing apparatus 7.
  • the abnormality diagnosing apparatus 7 includes a low pass filter 70, a mother wavelet deriving section 71, a wavelet transformation section 72, a correlation calculation section 73, and an abnormality determining section 74.
  • the abnormality diagnosing apparatus 7 further includes an abnormal waveform storage section 75 and a mother wavelet storage section 76. Furthermore, the abnormality diagnosing apparatus 7 is provided with a display section 8 for displaying the progress of diagnosis and the diagnosis result, as shown in FIG. 1 .
  • a sound detection signal at the evaluation point acquired by the microphone 6 is filtered by the low pass filter 70, so that a waveform signal is extracted.
  • This extracted signal (referred to as an evaluation waveform signal S in the following description) serves as a sample where high frequency noise is removed.
  • the mother wavelet deriving section 71 derives a mother wavelet from the evaluation waveform signal S, and then provides the mother wavelet to the wavelet transformation section 72.
  • the abnormal waveform storage section 75 stores an abnormal waveform signal, that is, an evaluated waveform signal. This signal is extracted by filtering a sound detection signal acquired at the evaluation point (where the microphone 6 is arranged) when abnormality arises in each of the vibration sources described above and thereby removing high frequency noise.
  • the following description is given under the assumption that the abnormal waveform storage section 75 stores three kinds of abnormal waveform signals V 1 , V 2 , and V 3 extracted individually from a sound detection signal under a condition that each of the three vibration sources in the electric power steering device shown in FIG. 1 is in an abnormal state.
  • the three vibration sources are: the transmission mechanism from the steering assistance motor 5 to the steering shaft 3; the engagement part between the pinion shaft 2 and the rack shaft 1; and the support part of the rack shaft 1 on the one side of the rack housing 10.
  • a larger number of abnormal waveform signals are present.
  • the following procedure is performed for all of these signals.
  • FIG. 3 is a flow chart showing a procedure of implementation of an abnormality diagnosing method according to the present invention performed in the abnormality diagnosing apparatus 7.
  • the abnormality diagnosing apparatus 7 in FIG. 2 is illustrated as shown in the block diagram. However, the abnormality diagnosing apparatus 7 is actually constructed from an arithmetic processing unit composed of a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.
  • the following procedure shown as functional blocks executed in the mother wavelet deriving section 71, the wavelet transformation section 72, the correlation calculation section 73, and the abnormality determining section 74 are performed as the operation of the CPU in accordance with a program stored in the ROM.
  • a computer program recorded on an appropriate recording medium may be loaded to a general purpose computer, so that the abnormality diagnosing apparatus 7 may be implemented.
  • the abnormality diagnosing apparatus 7 monitors a noise detection signal at the evaluation point acquired by the microphone 6. Then, when a detection signal serving as the target for detection is generated, the abnormality diagnosing apparatus 7 acquires the detection signal (Step 1). Then, the abnormality diagnosing apparatus 7 filters the detection signal, and thereby extracts an evaluation waveform signal S (Step 2).
  • FIGS. 4A-4D are diagrams showing examples of the evaluation waveform signal and the abnormal waveform signals.
  • the horizontal axis indicates the time, while the vertical axis indicates the amplitude.
  • the evaluation waveform signal S shown in FIG. 4A and the three abnormal waveform signals V 1 , V 2 , and V 3 shown in FIGS. 4B, 4C, and 4D have the same time axis.
  • the abnormality diagnosing apparatus 7 derives a real signal mother wavelet, more specifically, a real signal mother wavelet of complex type, from the evaluation waveform signal S extracted as described above (Step 3).
  • the derivation of the real signal mother wavelet is performed using a waveform that is within a range satisfying a predetermined magnitude condition among the evaluation waveform signal S.
  • the evaluation waveform signal S may be displayed on the display section 8 in a manner shown in FIG. 4A , and then the selection may be performed by an operator who recognizes visually this display.
  • a predetermined magnitude condition may be stored in the RAM of the abnormality diagnosing apparatus 7 in advance, and then a use range satisfying this magnitude condition may be selected automatically.
  • a real signal mother wavelet ⁇ (t) is a function given by the following Eq. (1).
  • Quantity a in this Eq. (1) is a scale parameter corresponding to the inverse of the frequency, while b is a time parameter.
  • the mother wavelet ⁇ (t) need satisfy an admissible condition given by Eq. (2) in order that reconstruction of the signal should be possible.
  • FIG. 5 is a flow chart showing the procedure of derivation of the mother wavelet ⁇ (t).
  • the evaluation waveform signal S is provided, and then the use range is selected as described above.
  • Hanning window processing is performed on the waveform signal within the selected range in order that the condition of Eq. (2) should be satisfied (Step 11).
  • Fourier transformation is performed (Step 12).
  • the mother wavelet ⁇ (t) derived in this manner has a start point and an end point at zeros. Further, its average over the domain is zero, while the function is bounded.
  • the complex type mother wavelet ⁇ (t) is given by Eq. (4).
  • the real type mother wavelet ⁇ R (t) is processed by Fourier transformation, so that the frequency spectrum ⁇ R (f) hat is obtained.
  • the abnormality diagnosing apparatus 7 On completion of the above-mentioned procedure of deriving the mother wavelet ⁇ (t), more specifically, the real function mother wavelet of complex type, the abnormality diagnosing apparatus 7 then performs wavelet transformation on the abnormal waveform signals V 1 , V 2 , and V 3 given as shown in FIGS. 4B, 4C, and 4D , by using the mother wavelet ⁇ (t) (Step 4). Then, on the basis of this transformation result, the abnormality diagnosing apparatus 7 calculates correlation values between the abnormal waveform signals V 1 , V 2 , and V 3 , and the mother wavelet ⁇ (t) (Step 5).
  • the wavelet transformation formula expressed by Eq. (5) is used, which contains the mother wavelet ⁇ (t) derived from the evaluation waveform signal S at Step 3. Then, each of the abnormal waveform signals V 1 , V 2 , and V 3 is applied to the transform function f(t) in this equation, so that the transformation is performed.
  • the wavelet transformation performed as described above is the processing of obtaining the inner product between the transform function f(t) to which each of the abnormal waveform signals V 1 , V 2 , and V 3 is applied and the mother wavelet ⁇ (t).
  • the transform function f(t) agrees with the mother wavelet ⁇ (t)
  • the inner product is unity, while when disagrees, the inner product is zero.
  • the time parameter b is changed variously, correlation between the evaluation waveform signal S and each of the abnormal waveform signals V 1 , V 2 , and V 3 becomes clear on the time axis.
  • the mother wavelet ⁇ (t) is normalized.
  • the correlation values become larger than unity in accordance with the ratio.
  • correlation can be obtained also for a frequency component of the mother wavelet ⁇ (t).
  • instantaneous correlation can be obtained that indicates the degree of correlation including the component and the magnitude with respect to the evaluation waveform signal S used in the derivation of the mother wavelet ⁇ (t).
  • the abnormal waveform signals V 1 , V 2 , and V 3 are stored in the abnormal waveform storage section 75, while the mother wavelet ⁇ (t) is stored in the mother wavelet storage section 76.
  • the calculation of the correlation values at Step 5 can be performed sequentially by using the values stored in these storage sections 75 and 76.
  • the mother wavelet ⁇ (t) used in the present invention is a real signal mother wavelet of complex type derived on the basis of the detection result of the noise acquired actually at the evaluation point.
  • the correlation value calculated at Step 5 for each of the plurality of abnormal waveform signals V 1 , V 2 , and V 3 processed by wavelet transformation at Step 4 expresses correctly the degree of correlation between the present noise at the evaluation point and the noise acquired at the time of occurrence of abnormality in each vibration source.
  • the result of the wavelet transformation contains time information, and hence the generation time can also be determined.
  • the plurality of abnormal waveform signals V 1 , V 2 , and V 3 contain the same or near frequency component, the difference becomes clear in the degree of correlation of each signal with the evaluation waveform signal S.
  • the abnormality diagnosing apparatus 7 determines the presence or absence of abnormality by using the calculated correlation values (Step 6), and then completes a series of the analysis operation.
  • Step 6 when the target noise or vibration is known in advance, or alternatively when the real signal mother wavelet has already been derived, Steps 1 and 3 may be skipped. In this case, analysis may be performed in a state that the real signal mother wavelet is stored in the memory in the CPU.
  • the abnormality determination at Step 6 is performed by comparing the result of wavelet transformation for each of the abnormal waveform signals V 1 , V 2 , and V 3 with a predetermined criterion.
  • the result of this determination may be displayed on the display section 8 in an appropriate manner.
  • FIGS. 6A-6C are diagrams showing display examples, where the correlation values calculated for the three abnormal waveform signals V 1 , V 2 , and V 3 are displayed in the form of graphs on the same time axis.
  • the correlation value of the abnormal waveform signal V 1 shown in FIG. 6A is large. Thus, it is concluded that the noise detected at the evaluation point is caused by abnormality corresponding to the abnormal waveform signal V 1 .
  • the result of determination at Step 6 can be expressed as the display shown in FIGS. 6A-6C , and thereby reported to the operator.
  • the criterion (threshold) used in the determination at Step 6 may be stored in the form of a database in the abnormality diagnosing apparatus 7. This permits more accurate determination.
  • the above-mentioned embodiment has been described for an example of analysis in which the presence or absence of abnormality in vibration sources located in individual sections of an electric power steering device is determined on the basis of a sound detection result inside a car cabin.
  • the abnormality diagnosing method and the abnormality diagnosing apparatus according to the present invention are applicable to general usage in which the presence or absence of abnormality in sound sources or vibration sources located in the periphery of a target space is determined on the basis of a detection result of sound or vibration at an appropriate evaluation point in the target space.
  • the present invention can be used widely in various fields of industry.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Claims (4)

  1. Procédé de diagnostic d'anomalies sur son ou vibration destiné à analyser un son ou vibration qui apparaît au niveau d'un point d'évaluation agencé dans un espace cible contenant une pluralité de sources sonores ou de sources de vibration, et de cette manière, à diagnostiquer la présence ou l'absence d'une anomalie se produisant sur la source sonore ou la source de vibration, caractérisé en ce qu'il comprend :
    une étape préliminaire d'extraction de signaux de forme d'onde anormaux individuellement à partir d'un résultat de détection de son ou vibration acquis au préalable au point d'évaluation, au moment de l'occurrence de l'anomalie sur chacune de la pluralité de sources sonores ou de sources de vibration ;
    une première étape (S2) d'extraction d'un signal de forme d'onde d'évaluation servant de cible d'analyse à partir d'un résultat de détection de son ou vibration au point d'évaluation ;
    une deuxième étape (S3) d'exécution d'un traitement sur fenêtre de Hann, transformation de Fourrier, traitement de normalisation, et transformation de Hilbert sur le signal de forme d'onde d'évaluation extrait à ladite première étape (S2), et, de cette manière, de déduction d'une ondelette mère de signal réel de type complexe ;
    une troisième étape (S4, S5) d'exécution d'une transformation en ondelette sur chacun des signaux de forme d'onde anormaux extraits à ladite étape préliminaire en utilisant l'ondelette mère de signal réel déduite à ladite deuxième étape (S3), et, de cette manière, de calcul d'une valeur de corrélation de chaque signal de forme d'onde anormale avec l'ondelette mère de signal réel ;
    une étape (S6) de comparaison de la valeur de corrélation calculée à ladite troisième étape (S4, S5) avec un critère prédéterminé, et, de cette manière, de détermination de la présence ou de l'absence d'anomalie sur chacune de la pluralité de sources sonores ou de sources de vibration ; et
    une étape d'affichage de l'un du résultat de calcul obtenu à ladite troisième étape (S4, S5) et du résultat de détermination obtenu à ladite étape (S6) de comparaison ou des deux.
  2. Dispositif de diagnostic d'anomalie sur son ou vibration destiné à analyser un son ou vibration qui apparaît au niveau d'un point d'évaluation agencé dans un espace cible contenant une pluralité de sources sonores ou de sources de vibration, et, de cette manière, à diagnostiquer la présence ou l'absence d'une anomalie se produisant sur la source sonore ou la source de vibration, caractérisé par le fait qu'il comprend :
    un moyen de détection (6) de son ou vibration agencé au point d'évaluation ;
    un premier moyen de mémorisation (75) destiné à mémoriser des signaux de forme d'onde anormaux extraits individuellement d'un résultat de détection dudit moyen de détection (6) au moment de l'occurrence d'une anomalie sur chacune de la pluralité de sources sonores ou de sources de vibration ;
    un moyen d'extraction (70) destiné à extraire un signal de forme d'onde d'évaluation servant de cible d'analyse à partir du résultat de détection dudit moyen de détection (6) ;
    un moyen de déduction (71) destiné à exécuter un traitement sur fenêtre de Hann, une transformation de Fourrier, un traitement de normalisation, et une transformation de Hilbert sur le signal de forme d'onde d'évaluation extrait par ledit moyen d'extraction (70), et, de cette manière, à déduire une ondelette mère de signal réel du type complexe ;
    un moyen de calcul de corrélation (72, 73) destiné à exécuter une transformation en ondelette sur chacun des signaux de forme d'onde anormaux mémorisés dans ledit premier moyen de mémorisation (75) en utilisant l'ondelette mère de signal réel déduite par ledit moyen de déduction (71), et, de cette manière, à calculer une valeur de corrélation de chaque signal de forme d'onde anormal avec l'ondelette mère de signal réel ;
    un moyen de détermination (74) destiné à comparer le résultat de calcul dudit moyen de calcul de corrélation (72, 73) à un critère prédéterminé, et, de cette manière, à déterminer la présence ou l'absence d'une anomalie sur chacune de la pluralité de sources sonores ou de sources de vibration ; et
    un moyen d'affichage (8) destiné à afficher l'un du résultat de calcul obtenu par ledit moyen de calcul de corrélation (72, 73) et du résultat de détermination obtenu par ledit moyen de détermination (74) ou les deux.
  3. Dispositif de diagnostic d'anomalie selon la revendication 2,
    caractérisé par le fait qu'il comprend, en outre :
    un second moyen de mémorisation (76) destiné à mémoriser une ondelette mère de signal réel déduite par ledit moyen de déduction (71), dans lequel ledit moyen de calcul de corrélation (72, 73) exécute une transformation en ondelette sur chacun des signaux de forme d'onde anormaux mémorisés dans ledit premier moyen de mémorisation (75) en utilisant l'ondelette mère de signal réel mémorisée dans ledit second moyen de mémorisation (76), et, de cette manière, calcule une valeur de corrélation de chaque signal de forme d'onde anormal avec l'ondelette mère de signal réel.
  4. Dispositif de diagnostic d'anomalie sur son ou vibration selon la revendication 2 ou 3, dans lequel
    ledit moyen d'affichage (8) affiche sur le même axe des temps, les valeurs de corrélation calculées par ledit moyen de calcul de corrélation (72, 73).
EP07290125.9A 2006-02-01 2007-01-31 Procédé et appareil de diagnostic d'anomalies pour son ou vibration Active EP1816451B1 (fr)

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JP2006025154A JP5063005B2 (ja) 2006-02-01 2006-02-01 音又は振動の異常診断方法及び音又は振動の異常診断装置

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EP1816451A2 EP1816451A2 (fr) 2007-08-08
EP1816451A3 EP1816451A3 (fr) 2012-03-14
EP1816451B1 true EP1816451B1 (fr) 2013-06-19

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WO2010041526A1 (fr) * 2008-10-09 2010-04-15 国立大学法人豊橋技術科学大学 Dispositif de détection de signaux, procédé de détection de signaux et procédé de fabrication d’un dispositif de détection de signaux
TWI420089B (zh) * 2010-06-22 2013-12-21 Univ Southern Taiwan Tech 應用模態間包絡訊號之相關係數值於機械損壞診斷的方法
JP5783808B2 (ja) * 2011-06-02 2015-09-24 三菱電機株式会社 異常音診断装置
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JP2007205885A (ja) 2007-08-16
US20070176759A1 (en) 2007-08-02

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